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Jingxuan Long

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Proceedings Article | 23 November 2022 Paper

A joint scoring and classification for rehabilitation training poses based on convolutional neural networks

Jingxuan Long, Chuang Ma, Fengke Zhan

Proceedings Volume 12454, 124541T (2022) https://doi.org/10.1117/12.2658277

KEYWORDS: Convolutional neural networks, Neural networks, RGB color model, Convolution, Telemedicine, Sensors, Neurons, Visualization, Chronic obstructive pulmonary disease, Therapeutics

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Telemedicine is likely to be an increasingly available medical mean for modern patients and those who are inconvenient to go to hospitals. As a kind of Chinese traditional rehabilitation training, eight-section brocade is more suitable for patients of most ages and they can promote physical quality. However, it is difficult for non-professional patients to complete standard posture without doctor guidance. Therefore, in this work, a method of remote data collection, posture scoring, and classification based on convolutional neural network of Siamese Network will be presented. 1, 204 images are used as template pose data and other 1, 204 pictures are patient’s poses. To train the network to have a better understanding of the image's sophistication, a Convolutional Neural Network (CNN) is used in this work, which can take an input image and apply learnable weights and biases to the image's numerous objects. Meanwhile, Siamese network is mentioned as a method to compare two vectors with different outputs so that posture classification and evolution can be realized. The average similarity of the score is 0.2172, as can be shown. The score range is -1 to 1. The closer the score is to 0, the more the patient's activities resemble the template.

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